ReviewFrontiers in plant science2021
Intelligent Fruit Yield Estimation for Orchards Using Deep Learning Based Semantic Segmentation Techniques-A Review.
Review in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Research progress on precision orchard yield estimation based on multi-source information perception sensors.Frontiers in plant science · 2026Pooled it
- Plant counting: origins, development, and future paths.Plant phenomics (Washington, D.C.) · 2026Review
- A review: research progress on intelligent technologies for orchard yield monitoring.Frontiers in plant science · 2026Review
- A Detection Approach for Wheat Spike Recognition and Counting Based on UAV Images and Improved Faster R-CNN.Plants (Basel, Switzerland) · 2025Article
- A lightweight tri-modal few-shot detection framework for fruit diversity recognition toward digital orchard archiving.Frontiers in plant science · 2025Article
- Review
- 3D Camera and Single-Point Laser Sensor Integration for Apple Localization in Spindle-Type Orchard Systems.Sensors (Basel, Switzerland) · 2024Article
- Transforming Poultry Farming: A Pyramid Vision Transformer Approach for Accurate Chicken Counting in Smart Farm Environments.Sensors (Basel, Switzerland) · 2024Article
- CucumberAI: Cucumber Fruit Morphology Identification System Based on Artificial Intelligence.Plant phenomics (Washington, D.C.) · 2024Article
- YOLOC-tiny: a generalized lightweight real-time detection model for multiripeness fruits of large non-green-ripe citrus in unstructured environments.Frontiers in plant science · 2024Article
- Adaptive spatial-channel feature fusion and self-calibrated convolution for early maize seedlings counting in UAV images.Frontiers in plant science · 2024Article
- Geographic-Scale Coffee Cherry Counting with Smartphones and Deep Learning.Plant phenomics (Washington, D.C.) · 2024Article
- Towards the synthesis of spectral imaging and machine learning-based approaches for non-invasive phenotyping of plants.Biophysical reviews · 2023Review
- Impact of Cultivation and Origin on the Fruit Microbiome of Apples and Blueberries and Implications for the Exposome.Microbial ecology · 2023Article
- YOLO POD: a fast and accurate multi-task model for dense Soybean Pod counting.Plant methods · 2023Article
- Advancement in artificial intelligence for on-farm fruit sorting and transportation.Frontiers in plant science · 2023Review
- Research on multi-cluster green persimmon detection method based on improved Faster RCNN.Frontiers in plant science · 2023Article
- Fruit Volume and Leaf-Area Determination of Cabbage by a Neural-Network-Based Instance Segmentation for Different Growth Stages.Sensors (Basel, Switzerland) · 2022Article
- Fast Location and Recognition of Green Apple Based on RGB-D Image.Frontiers in plant science · 2022Article
- Apple detection and instance segmentation in natural environments using an improved Mask Scoring R-CNN Model.Frontiers in plant science · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Smart farming employs intelligent systems for every domain of agriculture to obtain sustainable economic growth with the available resources using advanced technologies. Deep Learning (DL) is a sophisticated artificial neural network architecture that provides state-of-the-art results in smart farming applications. One of the main tasks in this domain is yield estimation. Manual yield estimation undergoes many hurdles such as labor-intensive, time-consuming, imprecise results, etc. These issues motivate the development of an intelligent fruit yield estimation system that offers more benefits to the farmers in deciding harvesting, marketing, etc. Semantic segmentation combined with DL adds promising results in fruit detection and localization by performing pixel-based prediction. This paper reviews the different literature employing various techniques for fruit yield estimation using DL-based semantic segmentation architectures. It also discusses the challenging issues that occur during intelligent fruit yield estimation such as sampling, collection, annotation and data augmentation, fruit detection, and counting. Results show that the fruit yield estimation employing DL-based semantic segmentation techniques yields better performance than earlier techniques because of human cognition incorporated into the architecture. Future directions like customization of DL architecture for smart-phone applications to predict the yield, development of more comprehensive model encompassing challenging situations like occlusion, overlapping and illumination variation, etc., were also discussed.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.